Sex, Drugs and Economics

Tuesday, 9 October 2018

The effect of cutting subsidies for after-hours doctors

The New Zealand Herald reported last week:

Parents who received free after-hours medical care for their children are now having to pay up to $61 at two Auckland clinics following funding cuts from district health boards...

The changes meant White Cross Glenfield's casual fee for under 13s after hours skyrocketed from free to $61.

At Three Kings Medical Centre, prices for care after 5pm had gone up to $50 for children aged between 6 to 12 - and $35 for under-6-year-olds.

This is what happens when you remove a subsidy - the price that consumers pay goes up. To see why, consider the market in the diagram below. The subsidy is paid to the supplier (the after-hours medical clinic), so we show it using the S-subsidy curve. The consumers (patients) pay the price where that curve meets the demand curve (PC), which from the article above could be as low as zero. The clinic receives that price (PC) from the patient, but then is topped up by the government subsidy, and receives an effective price of PP. The number of patients going to the clinic is Q1. If the subsidy is removed, the market shifts to equilibrium, where demand meets supply. The price for patients increases to P0, and the price received by clinics decreases to P0. The number of patients going to the clinic decreases to Q0.

The article notes that the subsidy hasn't been removed from all clinics. So, patients may simply go to some other clinic instead of the nearest one, if the nearest one is no longer subsidised. This was effectively what the DHB was trying to achieve:

Waitemata and Auckland City DHB announced a rejig to after-hours clinic funding in July in a bid to "reduce inequalities".

Presumably, that means that the DHB removed the subsidies from clinics in areas that are relatively more affluent (so that a higher proportion of the total subsidy goes to areas that are less affluent)? A more cynical view is that the DHB will benefit from some cost savings (which they may need!). The cost savings arise because fewer patients in total will go to after-hours clinics that are subsidised (if your illness isn't urgent or critical, maybe you choose not to go to the doctor, because the subsidised clinic is far away, and the unsubsidised clinic is now more expensive). The DHB also benefits from administration cost savings, because the DHB now has to deal with fewer clinics. The costs of the removed subsidy are borne by patients (their medical care is now more expensive, because it is unsubsidised, or because they have to travel further to get to a subsidised clinic) and the now-unsubsidised clinics (who receive a lower effective price from patients, and see fewer of them).

Another way of looking at who is made worse off by removing this subsidy is to consider economic welfare. Consumer (patient) surplus is the difference between what consumers are willing to pay for the service (shown by the demand curve) and the price they actually pay. In the diagram above, the consumer surplus is the triangle AEPC when there is a subsidy, but decreases to ABP0 when the subsidy is removed. Consumers (patients) are worse off without the subsidy.

Producer (clinic) surplus is the difference between the price that the producers receive and the producers' costs (shown by the supply curve). In the diagram above, the producer surplus is the triangle PPFG when there is a subsidy, but decreases to P0BG when the subsidy is removed. Producers (clinics) are worse off without the subsidy.

The taxpayer (the DHB) is the only party made better off without the subsidy. [*]

Finally, the loss of economic welfare is not the only cost of the removal of the subsidy. If patients are dissuaded from attending a clinic at all because of the higher cost, there could be real health losses that arise from the change in policy. It would be interesting to know how big an effect this has.

*****

[*] I have ignored what happens to total economic welfare in this diagram and this analysis. Typically, if we draw a subsidy on a market and the subsidy moves the market away from the quantity where marginal social benefit is equal to marginal social cost (as in the diagram I have shown), total economic welfare decreases (the subsidy makes society worse off, on aggregate). However, health care has positive externalities that are also not represented in the diagram, and in the presence of positive externalities a subsidy can actually increase (rather than decrease) total economic welfare. I've opted to keep the diagram simple by ignoring positive externalities and the effect on total welfare.

Saturday, 6 October 2018

Why study economics? Economists in tech companies edition...

In my ongoing series of posts entitled "Why study economics?" (see the end of this post for links), several times I have highlighted the increasing role of economists in technology companies. In a new NBER Working Paper (ungated version here), Susan Athey (previously chief economist at Microsoft, and now at Stanford and on the board of a number of technology companies) and Mike Luca (Harvard) provide a great overview of the intersection of economics (and economists) and technology companies:

PhD economists have started to play an increasingly central role in tech companies, tackling problems such as platform design, pricing, and policy. Major companies, including Amazon, eBay, Google, Microsoft, Facebook, Airbnb, and Uber, have large teams of PhD economists working to engineer better design choices. For example, led by Pat Bajari, Amazon has hired more than 150 Ph.D. economists in the past five years, making them the largest employer of tech economists. In fact, Amazon now has several times more full time economists than the largest academic economics department, and continues to grow at a rapid pace.

Importantly, it isn't just PhD economists:

Tech companies have also created strong demand for undergraduate economics majors, who take roles ranging from product management to policy.

What is it about economics that creates value for tech firms? Athey and Luca identify:

...three broad skillsets that are part of the economics curriculum that allow economists to thrive in tech companies: the ability to assess and interpret empirical relationships and work with data; the ability to understand and design markets and incentives, taking into account the information environment and strategic interactions; and the ability to understand industry structure and equilibrium behavior by firms.

One further interesting point is that:

...Amazon was the largest employer of Harvard Business School’s most recent graduating class of MBA students.

The job market for economics graduates (or, more broadly, graduates with skills in economics) is looking stronger.

[HT: Marginal Revolution]

Your Fitbit will betray you

Yesterday I wrote a post about how home insurers are starting to more accurately price house insurance based on natural hazard risk. Home insurance isn't the only area where insurers are looking at adopting more sophisticated screening methods to deal with adverse selection. Take this story from the New Zealand Herald in June:

Fitbits are already used to track your heart rate, the amount of exercise you do and how much you sleep - essential data that could potentially be used by insurance providers to determine your premiums.

The boom in wearable health tracking technology means we now have more information than ever before on health and well being of people at any given moment.

The Telegraph reports that information collected from these devices is already being used by insurers to calculate insurance premiums and there are concerns that this might lead to only the healthiest customers enjoying lower premiums.

This is serious business. Insurance companies have it in their interests not only to ensure the lowest-risk customers but also to detect potential health conditions before they become severe (and expensive). A study of the insurance market by the Swiss Re Institute, a research organisation, last year found that insurers had filed hundreds of patent applications relating to "predictive insurance modelling".

The issue that an uninformed health insurer or life insurer faces is essentially the same as the home insurer from yesterday's post. They can't tell the low-risk applicants from high-risk applicants. A pooling equilibrium develops, where everyone pays the same premiums (coarsely differentiated based on age, gender, and smoking status). A savvy and entrepreneurial insurer that was better able to tell who the low-risk insured people are could attract them away with lower premiums (knowing that they would cost less to insure because they are low risk).

So, that is effectively what insurers are starting to do. As the Herald article notes:

In making these moves, Insurance companies aim to collect data that could serve to help them make better policy decisions or even tweak existing policies over time.

The Telegraph reported that policy agreements increasingly feature clauses that allow insurers to collect data on their customers.

This is a point that I first made in a post back in 2015 (and an earlier post on technology in car insurance in 2014). We can all look forward to insurers asking for our Fitbit data when we apply for health or life insurance. And if we're fit and healthy, we'll give it to them. The people most likely to withhold that information are the unfit and unhealthy (and those who are most privacy-conscious). Denying access to your Fitbit data would probably be enough to signal to the insurer that you are high risk, and result in a declined application or a higher premium. So, even if you want to opt out of sharing your data, your Fitbit will still betray you.

Read more:

Monday, 1 October 2018

The most surprising thing I learned about home insurance this year

Home insurance markets are subject to adverse selection problems. When a homeowner approaches an insurer about getting home insurance, the insurer doesn't know whether the house is low-risk or high-risk. [*] The riskiness of the house is private information. In fact, the riskiness of the house is probably not known even to the homeowner, but let's assume for the moment that they have at least some idea. To minimise the risk to themselves of engaging in an unfavourable market transaction, it makes sense for the insurer to assume that every house is high-risk. This leads to a pooling equilibrium - low-risk houses are grouped together with the high-risk houses and owners of both types of house pay the same premium, because they can't easily differentiate themselves. This creates a problem if it causes the market to fail.

The market failure may arise as follows (this explanation follows Stephen Landsburg's excellent book The Armchair Economist). Let's say you could rank every house from 1 to 10 in terms of risk (the least risky are 1's, and the most risky are 10's). The insurance company doesn't know who is high-risk or low-risk. Say that they price the premiums based on the 'average' risk ('5' perhaps). The low risk homeowners (1's and 2's) would be paying too much for insurance relative to their risk, so they choose not to buy insurance. This raises the average risk of the homes of those who do buy insurance (to '6' perhaps). So, the insurance company has to raise premiums to compensate. This causes some of the medium risk homeowners (3's and 4's) to drop out of the market. The average risk has gone up again, and so do the premiums. Eventually, either only highest risk homeowners (10's) buy insurance, or no one buys it at all. This is why we call the problem adverse selection - the insurance company would prefer to insure low-risk homes, but it's the homeowners with high-risk homes who are most likely to buy.

Of course, insurers are not stupid. They've found ways to deal with this adverse selection problem. When the uninformed party (the insurer in this case) tries to reveal the private information (about the riskiness of the house), we refer to this as screening. Screening involves the insurer collecting information about the house and the homeowner in order to work out how risky the house is. With the private information revealed, the insurer can then price accordingly - higher-risk houses attract higher premiums, while lower-risk houses attract lower premiums. We have a separating equilibrium (the high-risk and low-risk houses are separated from each other in the market).

With all this in mind, this story from April surprised me greatly:

Other insurers are likely to follow NZX-listed Tower's lead and increase their focus on risk-based pricing for natural hazards, says an insurance expert...

Thousands of home-owners who live in high-risk earthquake-prone areas and insure via Tower are set to face hikes in their premiums while those in low-risk areas like Auckland will get a cut.

The insurance company, which is New Zealand's third largest general insurer, said it would stop cross-subsidising its policy-holders from April 1 in a bid to send a clearer message to home-owners about the risks of where they lived.

Tower chief executive Richard Harding said at the moment six Auckland households were paying more to subsidise insurance premiums for every one high-risk house in Wellington, Canterbury or Gisborne.

In other words, insurers previously weren't screening for all available private information before pricing their insurance for a given house. Essentially, owners of low-risk houses have been paying premiums that are too high, and owners of high-risk houses have been paying premiums that are too low. It took a little while, but eventually other insurers have also started to use risk assessments in determining insurance premiums, so this discrepancy is disappearing.

Why didn't the market break down due to adverse selection? The issue here is something I noted earlier in the post - the riskiness of a house is private information to both the insurer and the homeowner. If the homeowner doesn't know how risky their house is, owners of low-risk houses can't tell if the insurer is pricing their insurance too high relative to the risk of natural hazard damage. So, the owners of low-risk houses have no reason to drop out of the market. And, if the owners of low-risk houses don't drop out of the market, insurers have no reason to raise premiums.

However, that leaves the market open to disruption. As noted in the April article:

Jeremy Holmes, a principal at actuarial consulting firm Melville Jessup Weaver, said it was hard to say how long this would take. Insurers needed to be as good as their competitors at distinguishing risk.

"Otherwise they risk having their competitors target the lower-risk policyholders whilst they are left with only the higher risks ..."

An entrepreneurial insurer that was able to distinguish the low-risk houses from high-risk houses could start approaching owners of low-risk houses and offering them lower premiums. The remaining insurers would be left with higher-risk houses on average, and would have to raise premiums. This would increase the number of homeowners dropping out of the market (or rather, going to the insurer that was pricing according to risk). Tower was the first insurer to shift to risk-based premiums, so presumably they recognised this issue before any of the other insurers and acted exactly as you would expect - by moving to risk-based premiums before any potential disruptor could enter the market.

Still, it's a little surprising (to me, at least) that pricing based on natural hazard risk wasn't already happening.

*****

[*] For simplicity, I'm going to refer to low-risk houses and high-risk houses, when risk is probably as much a function of location as of the house itself. So, if you must, read 'low-risk house' as 'house with a low risk of damage in an earthquake', and 'high-risk house' as 'house with a high risk of damage in an earthquake'.